BACKGROUND: The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. ...
Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their binding interfaces remains a challenge. In this study, we present a ...
Drug combinations can be the prime strategy for increasing the initial treatment options in cancer therapy. However, identifying the combinations through experimental approaches is very laborious and costly. Notably, in vitro and/or in vivo examinati...
Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial...
Antibiotic-resistant strains are an emerging threat to public health. The usage of antimicrobial peptides (AMPs) is one of the promising approaches to solve this problem. For the development of new AMPs, it is necessary to have reliable prediction me...
Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied protei...
The Plant journal : for cell and molecular biology
Mar 29, 2023
Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of...
The remarkable recent advances in protein structure prediction have enabled computational modeling of protein structures with considerably higher accuracy than ever before. While state-of-the-art structure prediction methods provide self-assessment c...
The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and ...
BACKGROUND: Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research...
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